---
title: "Innovation Numérique Agricole au Québec"
author: "LRI"
output: 
  flexdashboard::flex_dashboard:
    theme : readable
    logo: CIRANO.jpeg
    source_code: embed
---

```{r setup, include=FALSE, echo=FALSE, message = FALSE, warning = FALSE}
knitr::opts_chunk$set(echo = TRUE)

#    runtime: shiny
library(dplyr)
library(readxl)
library(readr)
library(ggplot2)
library(ggthemes)
library(DT)
library(flexdashboard)
library(tidyverse)
library(lubridate)
library(tidyr)
library(plotly)
library("stringr")
library("plyr")
library("readr")
library(Req)
library(leaflet)
library(dplyr)
library(factoextra)
library(geojsonsf)
library(shiny)
library(RColorBrewer)
library(mapview)
library(webshot)

library(leafem)

## On importe les données concernant l'écosystème
ecosysteme <- gsheet::gsheet2tbl("https://docs.google.com/spreadsheets/d/1XwSgrbq5P6CKFkroaciYULswxxGA0aPig1Yojcrt6tM/edit?usp=sharing")
ecosysteme$Moyenne <- as.numeric(ecosysteme$Moyenne)




```

```{r, echo=FALSE, message = FALSE, warning = FALSE,include = FALSE}
## On créé la matrice de distance avec le centroïde de l'écosystème
distance <- data.frame(matrix(ncol=3, nrow=0))
colnames(distance) <- c("Categorie","Longitude","Latitude")

distance[1,] <- c(ecosysteme$Nom_ent[2],ecosysteme$Longitude[2],ecosysteme$Latitude[2])
distance$Longitude <- round(as.numeric(distance$Longitude),5)
distance$Latitude <- round(as.numeric(distance$Latitude),5)
distance[1,4] <- "Écosystème" 
distance[1,5:11] <- 1
colnames(distance)[4] <- "Secteur"
colnames(distance)[5] <- "Complet" ## Catégorie prenant tous les exploitations du secteur agricole
colnames(distance)[6] <- "emp_sup_10" ## Catégorie des exploitations de plus de 10 employés
colnames(distance)[7] <- "emp_inf_10" ## Catégorie des exploitations de moins de 10 employés
colnames(distance)[8] <- "age_inf_5" ## Catégorie des entreprises agricoles de moins de 5 ans
colnames(distance)[9] <- "age_inf_15_sup_5" ## Catégorie des entreprises agricoles de plus de 5 ans et moins de 15 ans
colnames(distance)[10] <- "age_sup_15" ## Catégorie des entreprises agricoles de plus de 15 ans
colnames(distance)[11] <- "Dist_centre" ## Distance avec le centroïde de la grappe industrielle
```

# Ecosystème

## Column {data-width="300"}

### Présentation

<style>
body {
text-align: justify}
</style>

**Bienvenue sur le tableau de bord associé à mon mémoire de maîtrise !**

Ce document numérique a pour but de faciliter la lecture des résultats issus de mon mémoire de maîtrise. 

Pour rappel, mon sujet de recherche s'intitule : 

*Agriculture 4.0 et Grappes Industrielles d'Innovation : Une Analyse de Science des Données Géospatiales*

Chaque onglet de ce tableau de bord a pour but de présenter un aspect de la méthodologie de recherche, les figures sont interactives et présentent des informations complémentaires lorsque l'on clique sur les différents éléments. Les définitions nécessaires à comprendre la totalité des éléments du tableau de bord sont présentées dans le chapitre 3 de méthodologie du mémoire.


Les onglets présentent les informations suivantes :

- "Écosystème" présente le positionnement géographique des entités identifiées comme appartenant à l'écosystème d'innovation numérique agricole
- "Exemples sectoriels" présente les différentes variables représentés et calculés pour des secteurs donnés.
- "Cartographie des secteurs" présente de façon globale les variations de distance et de positionnements en fonction des secteurs agricoles
- "Analyse des variables" présente comment les variables d'âge et du nombre d'employés impactent les distances moyennes au signal de l'écosystème et les écart-types associés
- "Repenser l'agriculture : Clusters agricoles" présente les résultats d'un partitionnement des exploitations agricoles pour minimiser les distances entre les exploitations ainsi que la composition sectorielle des clusters obtenus

Le code ayant donné lieu à ces résultats est visible dans l'onglet "Source code" et a été codé en R sur la plateforme Rstudio mise à ma disposition par Nuance_R.

Bonne lecture !

LRI

### {data-height="75"}

![](./polytechnique_gauche_rgb_2.jpeg)

## Column {data-width="700"}

### Cartographie du positionnement des entités de la grappe industrielle d'innovation numérique et influences associées

```{r, echo=FALSE, message = FALSE, warning = FALSE}

ecosysteme <- ecosysteme[-1,]

ecosysteme$Moyenne2 <- round(ecosysteme$Moyenne^2,2)

## On cartographie l'écosystème
leaflet(data = ecosysteme) %>% 
  setView(-72.8, 46.1, 8) %>%
  addTiles() %>% 
  addCircleMarkers(lng = ~Longitude, lat = ~Latitude, ## On ajoute les points de coordonnées des entreprises
      radius = ~Moyenne2, stroke = FALSE, fillOpacity = 0.8, 
      color= ~colorBin('OrRd',Moyenne)(Moyenne), 
      popup = ~ paste("Nom:", Nom_ent, "<br/>","Signal :", Moyenne,"<br/>","Impact des services :", Impact)) %>%
  addCircleMarkers(lng = ecosysteme$Longitude[1], lat = ecosysteme$Latitude[1], ## On ajoute le centroïde de l'écosystème
      radius = 25, stroke = FALSE, fillOpacity = 0.9, 
      color = "steelblue",
      popup = paste("Nom:", ecosysteme$Nom_ent[1], "<br/>","Signal :", ecosysteme$Moyenne[1],"<br/>","Impact des services :", ecosysteme$Impact[1]) )  %>%
  addLegend(
    "bottomright",
    title = "Signal de l'entreprise",
    pal = colorBin('OrRd', ecosysteme$Moyenne),
    values = ecosysteme$Impact, 
    opacity = 0.9) 
    
  
```

# Céréales {data-navmenu="Exemples sectoriels"}

## Column {data-width="570"}

### Cartographie du positionnement du centre des sous-secteurs d'activité céréalière

```{r, echo=FALSE, message=FALSE,warning=FALSE}
df_0131 <- Req::Req_data(industry = 0131, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r, echo=FALSE, message = FALSE, warning = FALSE}

distance[2,1] <- "Céréales"
distance[2,2] <- round(mean(df_0131$Long,na.rm = TRUE),5)
distance[2,3] <- round(mean(df_0131$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[2,4] <- "Céréales"
distance[2,5] <- 1
distance[2,12] <- length(df_0131$Long) ## Calcul de nombre d'entreprises agricoles appartenant à la catégorie
distance[2,13] <- round(mean(sqrt((df_0131$Lat - mean(df_0131$Lat,na.rm = TRUE))^2 + (df_0131$Long - mean(df_0131$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[2,14] <- round(sd(sqrt((df_0131$Lat - mean(df_0131$Lat,na.rm = TRUE))^2 + (df_0131$Long - mean(df_0131$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[2,15] <- round(sd(sqrt((df_0131$Lat - ecosysteme$Latitude[1])^2 + (df_0131$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[2,16] <- round(mean(sqrt((df_0131$Long - ecosysteme$Longitude)^2 + (df_0131$Lat - ecosysteme$Latitude)^2)),4)


index <- df_0131 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[3,1] <- "Céréales & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[3,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[3,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[3,4] <- "Céréales"
distance[3,6] <- 1
distance[3,12] <- length(index$Long)
distance[3,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[3,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[3,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[3,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0131 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[4,1] <- "Céréales & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[4,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[4,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[4,4] <- "Céréales"
distance[4,7] <- 1
distance[4,12] <- length(index$Long)
distance[4,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[4,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[4,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[4,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0131 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[5,1] <- "Céréales & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 5 ans
distance[5,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[5,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[5,4] <- "Céréales"
distance[5,8] <- 1
distance[5,12] <- length(index$Long)
distance[5,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[5,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[5,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[5,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0131 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[6,1] <- "Céréales & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[6,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[6,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[6,4] <- "Céréales"
distance[6,9] <- 1
distance[6,12] <- length(index$Long)
distance[6,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[6,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[6,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[6,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0131 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[7,1] <- "Céréales & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[7,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[7,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[7,4] <- "Céréales"
distance[7,10] <- 1
distance[7,12] <- length(index$Long)
distance[7,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[7,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[7,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[7,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

```

```{r, echo=FALSE, message = FALSE, warning = FALSE}
leaflet(data = distance[-1,]) %>%
  addTiles() %>% 
  addCircleMarkers(lng = ecosysteme$Longitude[1], lat = ecosysteme$Latitude[1], ## On ajoute le centroïde de l'écosystème
      radius = 25, stroke = FALSE, fillOpacity = 0.9, 
      color = "steelblue", popup = ecosysteme$Nom_ent[1]) %>%
  addAwesomeMarkers(popup = ~Categorie)
```

## Column {data-width="430"}

### Distance du centre des sous-secteurs avec le signal de l'écosystème et nombre d'exploitations associées

```{r, echo=FALSE, message = FALSE, warning = FALSE}
Long_centre <-distance$Longitude[1]
Lat_centre <- distance$Latitude[1]
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
colnames(distance)[11] <- "Dist_centre" ## On calcule la distance entre le centre de la catégorie et l'écosystème
colnames(distance)[12] <- "nb_ent" ## On compte le nombre d'entreprise remplissant les critères définis
colnames(distance)[13] <- "moyenne_dist" ## On calcule la distance moyenne entre les entreprises d'un secteur et son centre
colnames(distance)[14] <- "ecart_type_dist" ## On calcule l'écart type des distance entre les entreprises d'une catégorie donnée
colnames(distance)[15] <- "ecart_type_eco" ## On calcule l'écart type des distances entre les entreprises d'une catégorie et le centre de l'écosystème
colnames(distance)[16] <- "dist_moy_centre" ## On calcule la distance moyenne entre les entreprises d'un secteur et le centre de l'écosystème

```

```{r, echo=FALSE, message = FALSE, warning = FALSE}
datatable(distance[-1,c(1,11,12)],
            rownames = FALSE, 
              options = list(pageLength = 5, scrollX = TRUE,dom = 'tip'), 
              class = 'white-space: nowrap' )
```



### Cartographie de la distance moyenne des entreprises des sous-secteurs à l'écosystème et écart-type associé

```{r, echo=FALSE, message=FALSE,warning=FALSE}
p <- distance[] %>%
  filter(Secteur=="Céréales") %>%
    ggplot() +
    geom_point(aes(dist_moy_centre,ecart_type_eco,color=Categorie)) +
    ylab("Ecart-type") +
    xlab("Distance moyenne") +
    theme_minimal() 
      
  ggplotly(p)%>% #Ploty du graph
  config(displayModeBar = FALSE)
  
```





```{r Culture du maïs (sauf le maïs fourrager et le maïs sucré) (0134) , echo=FALSE, message=FALSE,warning=FALSE}
df_0134 <- Req::Req_data(industry = 0134, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```


```{r Culture du maïs (sauf le maïs fourrager et le maïs sucré) (0134) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[8,1] <- "Maïs"
distance[8,2] <- round(mean(df_0134$Long,na.rm = TRUE),5)
distance[8,3] <- round(mean(df_0134$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[8,5] <- 1
distance[8,12] <- length(df_0134$Long)
distance[8,13] <- round(mean(sqrt((df_0134$Lat - mean(df_0134$Lat,na.rm = TRUE))^2 + (df_0134$Long - mean(df_0134$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[8,14] <- round(sd(sqrt((df_0134$Lat - mean(df_0134$Lat,na.rm = TRUE))^2 + (df_0134$Long - mean(df_0134$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[8,15] <- round(sd(sqrt((df_0134$Lat - ecosysteme$Latitude[1])^2 + (df_0134$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[8,16] <- round(mean(sqrt((df_0134$Long - ecosysteme$Longitude)^2 + (df_0134$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0134 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[9,1] <- "Maïs & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[9,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[9,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[9,6] <- 1
distance[9,12] <- length(index$Long)
distance[9,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[9,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[9,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[9,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0134 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[10,1] <- "Maïs & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[10,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[10,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[10,7] <- 1
distance[10,12] <- length(index$Long)
distance[10,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[10,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[10,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[10,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0134 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[11,1] <- "Maïs & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[11,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[11,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[11,8] <- 1
distance[11,12] <- length(index$Long)
distance[11,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[11,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[11,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[11,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0134 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[12,1] <- "Maïs & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[12,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[12,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[12,9] <- 1
distance[12,12] <- length(index$Long)
distance[12,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[12,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[12,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[12,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0134 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[13,1] <- "Maïs & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[13,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[13,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[13,10] <- 1
distance[13,12] <- length(index$Long)
distance[13,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[13,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[13,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[13,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(8:13),4] <- "Maïs"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture de plantes fourragères (0135), echo=FALSE, message=FALSE,warning=FALSE}
df_0135 <- Req::Req_data(industry = 0135, active = 1)%>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture de plantes fourragères (0135) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[14,1] <- "Plantes fourragères"
distance[14,2] <- round(mean(df_0135$Long,na.rm = TRUE),5)
distance[14,3] <- round(mean(df_0135$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[14,5] <- 1 
distance[14,12] <- length(df_0135$Long)
distance[14,13] <- round(mean(sqrt((df_0135$Lat - mean(df_0135$Lat,na.rm = TRUE))^2 + (df_0135$Long - mean(df_0135$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[14,14] <- round(sd(sqrt((df_0135$Lat - mean(df_0135$Lat,na.rm = TRUE))^2 + (df_0135$Long - mean(df_0135$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[14,15] <- round(sd(sqrt((df_0135$Lat - ecosysteme$Latitude[1])^2 + (df_0135$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[14,16] <- round(mean(sqrt((df_0135$Long - ecosysteme$Longitude)^2 + (df_0135$Lat - ecosysteme$Latitude)^2)),4)


index <- df_0135 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[15,1] <- "Plantes fourragères & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[15,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[15,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[15,6] <- 1 
distance[15,12] <- length(index$Long)
distance[15,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[15,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[15,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[15,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0135 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[16,1] <- "Plantes fourragères & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[16,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[16,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[16,7] <- 1 
distance[16,12] <- length(index$Long)
distance[16,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[16,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[16,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[16,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0135 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[17,1] <- "Plantes fourragères & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[17,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[17,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[17,8] <- 1 
distance[17,12] <- length(index$Long)
distance[17,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[17,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[17,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[17,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0135 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[18,1] <- "Plantes fourragères & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[18,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[18,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[18,9] <- 1 
distance[18,12] <- length(index$Long)
distance[18,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[18,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[18,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[18,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0135 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[19,1] <- "Plantes fourragères & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[19,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[19,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[19,10] <- 1 
distance[19,12] <- length(index$Long)
distance[19,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[19,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[19,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[19,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(14:19),4] <- "Plantes fourragères"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture de pommes de terre (0138) , echo=FALSE, message=FALSE,warning=FALSE}
df_0138 <- Req::Req_data(industry = 0138, active = 1)%>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture de pommes de terre (0138) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[20,1] <- "Pommes de terre"
distance[20,2] <- round(mean(df_0138$Long,na.rm = TRUE),5)
distance[20,3] <- round(mean(df_0138$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[20,5] <- 1
distance[20,12] <- length(df_0138$Long)
distance[20,13] <- round(mean(sqrt((df_0138$Lat - mean(df_0138$Lat,na.rm = TRUE))^2 + (df_0138$Long - mean(df_0138$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[20,14] <- round(sd(sqrt((df_0138$Lat - mean(df_0138$Lat,na.rm = TRUE))^2 + (df_0138$Long - mean(df_0138$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[20,15] <- round(sd(sqrt((df_0138$Lat - ecosysteme$Latitude[1])^2 + (df_0138$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[20,16] <- round(mean(sqrt((df_0138$Long - ecosysteme$Longitude)^2 + (df_0138$Lat - ecosysteme$Latitude)^2)),4)


index <- df_0138 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[21,1] <- "Pommes de terre & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[21,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[21,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[21,6] <- 1
distance[21,12] <- length(index$Long)
distance[21,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[21,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[21,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[21,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0138 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[22,1] <- "Pommes de terre & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[22,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[22,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[22,7] <- 1
distance[22,12] <- length(index$Long)
distance[22,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[22,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[22,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[22,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0138 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[23,1] <- "Pommes de terre & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[23,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[23,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[23,8] <- 1
distance[23,12] <- length(index$Long)
distance[23,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[23,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[23,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[23,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0138 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[24,1] <- "Pommes de terre & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[24,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[24,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[24,9] <- 1
distance[24,12] <- length(index$Long)
distance[24,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[24,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[24,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[24,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0138 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[25,1] <- "Pommes de terre & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[25,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[25,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[25,10] <- 1
distance[25,12] <- length(index$Long)
distance[25,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[25,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[25,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[25,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(20:25),4] <- "Pommes de terre"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Autres grandes cultures (0139) , echo=FALSE, message=FALSE,warning=FALSE}
df_0139 <- Req::Req_data(industry = 0139, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Autres grandes cultures (0139) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[26,1] <- "Autres grandes cultures"
distance[26,2] <- round(mean(df_0139$Long,na.rm = TRUE),5)
distance[26,3] <- round(mean(df_0139$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[26,5] <- 1
distance[26,12] <- length(df_0139$Long)
distance[26,13] <- round(mean(sqrt((df_0139$Lat - mean(df_0139$Lat,na.rm = TRUE))^2 + (df_0139$Long - mean(df_0139$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[26,14] <- round(sd(sqrt((df_0139$Lat - mean(df_0139$Lat,na.rm = TRUE))^2 + (df_0139$Long - mean(df_0139$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[26,15] <- round(sd(sqrt((df_0139$Lat - ecosysteme$Latitude[1])^2 + (df_0139$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[26,16] <- round(mean(sqrt((df_0139$Long - ecosysteme$Longitude)^2 + (df_0139$Lat - ecosysteme$Latitude)^2)),4)
  
index <- df_0139 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[27,1] <- "Autres grandes cultures & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[27,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[27,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[27,6] <- 1
distance[27,12] <- length(index$Long)
distance[27,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[27,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[27,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[27,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0139 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[28,1] <- "Autres grandes cultures & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[28,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[28,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[28,7] <- 1
distance[28,12] <- length(index$Long)
distance[28,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[28,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[28,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[28,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0139 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[29,1] <- "Autres grandes cultures & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[29,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[29,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[29,8] <- 1
distance[29,12] <- length(index$Long)
distance[29,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[29,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[29,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[29,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0139 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[30,1] <- "Autres grandes cultures & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[30,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[30,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[30,9] <- 1
distance[30,12] <- length(index$Long)
distance[30,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[30,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[30,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[30,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0139 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[31,1] <- "Autres grandes cultures & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[31,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[31,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[31,10] <- 1
distance[31,12] <- length(index$Long)
distance[31,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[31,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[31,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[31,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(26:31),4] <- "Autres grandes cultures"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture de fruits (0151) , echo=FALSE, message=FALSE,warning=FALSE}
df_0151 <- Req::Req_data(industry = 0151, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture de fruits (0151) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[32,1] <- "Fruits"
distance[32,2] <- round(mean(df_0151$Long,na.rm = TRUE),5)
distance[32,3] <- round(mean(df_0151$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[32,5] <- 1
distance[32,12] <- length(df_0151$Long)
distance[32,13] <- round(mean(sqrt((df_0151$Lat - mean(df_0151$Lat,na.rm = TRUE))^2 + (df_0151$Long - mean(df_0151$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[32,14] <- round(sd(sqrt((df_0151$Lat - mean(df_0151$Lat,na.rm = TRUE))^2 + (df_0151$Long - mean(df_0151$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[32,15] <- round(sd(sqrt((df_0151$Lat - ecosysteme$Latitude[1])^2 + (df_0151$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[32,16] <- round(mean(sqrt((df_0151$Long - ecosysteme$Longitude)^2 + (df_0151$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0151 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[33,1] <- "Fruits & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[33,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[33,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[33,6] <- 1
distance[33,12] <- length(index$Long)
distance[33,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[33,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[33,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[33,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0151 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[34,1] <- "Fruits & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[34,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[34,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[34,7] <- 1
distance[34,12] <- length(index$Long)
distance[34,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[34,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[34,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[34,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0151 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[35,1] <- "Fruits & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[35,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[35,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[35,8] <- 1
distance[35,12] <- length(index$Long)
distance[35,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[35,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[35,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[35,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0151 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[36,1] <- "Fruits & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[36,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[36,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[36,9] <- 1
distance[36,12] <- length(index$Long)
distance[36,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[36,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[36,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[36,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0151 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[37,1] <- "Fruits & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[37,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[37,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[37,10] <- 1
distance[37,12] <- length(index$Long)
distance[37,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[37,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[37,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[37,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(32:37),4] <- "Fruits"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture de légumes (0152) , echo=FALSE, message=FALSE,warning=FALSE}
df_0152 <- Req::Req_data(industry = 0152, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture de légumes (0152) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[38,1] <- "Légumes"
distance[38,2] <- round(mean(df_0152$Long,na.rm = TRUE),5)
distance[38,3] <- round(mean(df_0152$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[38,5] <- 1 
distance[38,12] <- length(df_0152$Long)
distance[38,13] <- round(mean(sqrt((df_0152$Lat - mean(df_0152$Lat,na.rm = TRUE))^2 + (df_0152$Long - mean(df_0152$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[38,14] <- round(sd(sqrt((df_0152$Lat - mean(df_0152$Lat,na.rm = TRUE))^2 + (df_0152$Long - mean(df_0152$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[38,15] <- round(sd(sqrt((df_0152$Lat - ecosysteme$Latitude[1])^2 + (df_0152$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[38,16] <- round(mean(sqrt((df_0152$Long - ecosysteme$Longitude)^2 + (df_0152$Lat - ecosysteme$Latitude)^2)),4)


index <- df_0152 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[39,1] <- "Légumes & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[39,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[39,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[39,6] <- 1 
distance[39,12] <- length(index$Long)
distance[39,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[39,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[39,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[39,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0152 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[40,1] <- "Légumes & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[40,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[40,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[40,7] <- 1 
distance[40,12] <- length(index$Long)
distance[40,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[40,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[40,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[40,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0152 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[41,1] <- "Légumes & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[41,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[41,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[41,8] <- 1 
distance[41,12] <- length(index$Long)
distance[41,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[41,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[41,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[41,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)


index <- df_0152 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[42,1] <- "Légumes & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[42,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[42,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[42,9] <- 1 
distance[42,12] <- length(index$Long)
distance[42,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[42,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[42,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[42,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)


index <- df_0152 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[43,1] <- "Légumes & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[43,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[43,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[43,10] <- 1 
distance[43,12] <- length(index$Long)
distance[43,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[43,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[43,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[43,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)


##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(38:43),4] <- "Légumes"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture mixte de fruits et légumes (0159) , echo=FALSE, message=FALSE,warning=FALSE}
df_0159 <- Req::Req_data(industry = 0159, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture mixte de fruits et légumes (0159) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[44,1] <- "Mixte fruits et légumes"
distance[44,2] <- round(mean(df_0159$Long,na.rm = TRUE),5)
distance[44,3] <- round(mean(df_0159$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[44,5] <- 1 
distance[44,12] <- length(df_0159$Long)
distance[44,13] <- round(mean(sqrt((df_0159$Lat - mean(df_0159$Lat,na.rm = TRUE))^2 + (df_0159$Long - mean(df_0159$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[44,14] <- round(sd(sqrt((df_0159$Lat - mean(df_0159$Lat,na.rm = TRUE))^2 + (df_0159$Long - mean(df_0159$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[44,15] <- round(sd(sqrt((df_0159$Lat - ecosysteme$Latitude[1])^2 + (df_0159$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[44,16] <- round(mean(sqrt((df_0159$Long - ecosysteme$Longitude)^2 + (df_0159$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0159 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[45,1] <- "Mixte fruits et légumes & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[45,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[45,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[45,6] <- 1 
distance[45,12] <- length(index$Long)
distance[45,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[45,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[45,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[45,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)


index <- df_0159 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[46,1] <- "Mixte fruits et légumes & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[46,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[46,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[46,7] <- 1 
distance[46,12] <- length(index$Long)
distance[46,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[46,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[46,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[46,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)


index <- df_0159 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[47,1] <- "Mixte fruits et légumes & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[47,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[47,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[47,8] <- 1 
distance[47,12] <- length(index$Long)
distance[47,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[47,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[47,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[47,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)


index <- df_0159 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[48,1] <- "Mixte fruits et légumes & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[48,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[48,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[48,9] <- 1 
distance[48,12] <- length(index$Long)
distance[48,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[48,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[48,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[48,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)


index <- df_0159 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[49,1] <- "Mixte fruits et légumes & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[49,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[49,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[49,10] <- 1 
distance[49,12] <- length(index$Long)
distance[49,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[49,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[49,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[49,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)


##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(44:49),4] <- "Mixte fruits et légumes"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture de champignons (0161) , echo=FALSE, message=FALSE,warning=FALSE}
df_0161 <- Req::Req_data(industry = 0161, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture de champignons (0161) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[50,1] <- "Champignons"
distance[50,2] <- round(mean(df_0161$Long,na.rm = TRUE),5)
distance[50,3] <- round(mean(df_0161$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[50,5] <- 1
distance[50,12] <- length(df_0161$Long)
distance[50,13] <- round(mean(sqrt((df_0161$Lat - mean(df_0161$Lat,na.rm = TRUE))^2 + (df_0161$Long - mean(df_0161$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[50,14] <- round(sd(sqrt((df_0161$Lat - mean(df_0161$Lat,na.rm = TRUE))^2 + (df_0161$Long - mean(df_0161$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[50,15] <- round(sd(sqrt((df_0161$Lat - ecosysteme$Latitude[1])^2 + (df_0161$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[50,16] <- round(mean(sqrt((df_0161$Long - ecosysteme$Longitude)^2 + (df_0161$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0161 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[51,1] <- "Champignons & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[51,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[51,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[51,6] <- 1
distance[51,12] <- length(index$Long)
distance[51,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[51,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[51,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[51,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)


index <- df_0161 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[52,1] <- "Champignons & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[52,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[52,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[52,7] <- 1
distance[52,12] <- length(index$Long)
distance[52,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[52,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[52,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[52,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0161 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[53,1] <- "Champignons & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[53,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[53,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[53,8] <- 1
distance[53,12] <- length(index$Long)
distance[53,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[53,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[53,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[53,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0161 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[54,1] <- "Champignons & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[54,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[54,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[54,9] <- 1
distance[54,12] <- length(index$Long)
distance[54,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[54,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[54,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[54,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0161 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[55,1] <- "Champignons & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[55,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[55,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[55,10] <- 1
distance[55,12] <- length(index$Long)
distance[55,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[55,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[55,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[55,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(50:55),4] <- "Champignons"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture en serres (0162) , echo=FALSE, message=FALSE,warning=FALSE}
df_0162 <- Req::Req_data(industry = 0162, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture en serres (0162) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[56,1] <- "Serres"
distance[56,2] <- round(mean(df_0162$Long,na.rm = TRUE),5)
distance[56,3] <- round(mean(df_0162$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[56,5] <- 1
distance[56,12] <- length(df_0162$Long)
distance[56,13] <- round(mean(sqrt((df_0162$Lat - mean(df_0162$Lat,na.rm = TRUE))^2 + (df_0162$Long - mean(df_0162$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[56,14] <- round(sd(sqrt((df_0162$Lat - mean(df_0162$Lat,na.rm = TRUE))^2 + (df_0162$Long - mean(df_0162$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[56,15] <- round(sd(sqrt((df_0162$Lat - ecosysteme$Latitude[1])^2 + (df_0162$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[56,16] <- round(mean(sqrt((df_0162$Long - ecosysteme$Longitude)^2 + (df_0162$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0162 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[57,1] <- "Serres & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[57,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[57,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[57,6] <- 1
distance[57,12] <- length(index$Long)
distance[57,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[57,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[57,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[57,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0162 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[58,1] <- "Serres & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[58,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[58,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[58,7] <- 1
distance[58,12] <- length(index$Long)
distance[58,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[58,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[58,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[58,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0162 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[59,1] <- "Serres & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[59,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[59,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[59,8] <- 1
distance[59,12] <- length(index$Long)
distance[59,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[59,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[59,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[59,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0162 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[60,1] <- "Serres & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[60,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[60,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[60,9] <- 1
distance[60,12] <- length(index$Long)
distance[60,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[60,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[60,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[60,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0162 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[61,1] <- "Serres & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[61,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[61,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[61,10] <- 1
distance[61,12] <- length(index$Long)
distance[61,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[61,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[61,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[61,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(56:61),4] <- "Serres"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture en pépinières & gazonnières (0163) , echo=FALSE, message=FALSE,warning=FALSE}
df_0163 <- Req::Req_data(industry = 0163, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture en pépinières & gazonnières (0163) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[62,1] <- "Pépinières"
distance[62,2] <- round(mean(df_0163$Long,na.rm = TRUE),5)
distance[62,3] <- round(mean(df_0163$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[62,5] <- 1
distance[62,12] <- length(df_0163$Long)
distance[62,13] <- round(mean(sqrt((df_0163$Lat - mean(df_0163$Lat,na.rm = TRUE))^2 + (df_0163$Long - mean(df_0163$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[62,14] <- round(sd(sqrt((df_0163$Lat - mean(df_0163$Lat,na.rm = TRUE))^2 + (df_0163$Long - mean(df_0163$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[62,15] <- round(sd(sqrt((df_0163$Lat - ecosysteme$Latitude[1])^2 + (df_0163$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[62,16] <- round(mean(sqrt((df_0163$Long - ecosysteme$Longitude)^2 + (df_0163$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0163 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[63,1] <- "Pépinières & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[63,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[63,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[63,6] <- 1
distance[63,12] <- length(index$Long)
distance[63,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[63,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[63,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[63,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0163 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[64,1] <- "Pépinières & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[64,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[64,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[64,7] <- 1
distance[64,12] <- length(index$Long)
distance[64,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[64,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[64,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[64,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0163 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[65,1] <- "Pépinières & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[65,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[65,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[65,8] <- 1
distance[65,12] <- length(index$Long)
distance[65,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[65,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[65,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[65,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0163 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[66,1] <- "Pépinières & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[66,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[66,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[66,9] <- 1
distance[66,12] <- length(index$Long)
distance[66,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[66,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[66,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[66,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0163 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[67,1] <- "Pépinières & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[67,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[67,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[67,10] <- 1
distance[67,12] <- length(index$Long)
distance[67,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[67,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[67,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[67,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(62:67),4] <- "Pépinières"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture érabilières (0164) , echo=FALSE, message=FALSE,warning=FALSE}
df_0164 <- Req::Req_data(industry = 0164, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture érabilières (0164) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[68,1] <- "Érablières"
distance[68,2] <- round(mean(df_0164$Long,na.rm = TRUE),5)
distance[68,3] <- round(mean(df_0164$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[68,5] <- 1
distance[68,12] <- length(df_0164$Long)
distance[68,13] <- round(mean(sqrt((df_0164$Lat - mean(df_0164$Lat,na.rm = TRUE))^2 + (df_0164$Long - mean(df_0164$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[68,14] <- round(sd(sqrt((df_0164$Lat - mean(df_0164$Lat,na.rm = TRUE))^2 + (df_0164$Long - mean(df_0164$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[68,15] <- round(sd(sqrt((df_0164$Lat - ecosysteme$Latitude[1])^2 + (df_0164$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[68,16] <- round(mean(sqrt((df_0164$Long - ecosysteme$Longitude)^2 + (df_0164$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0164 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[69,1] <- "Érablières & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[69,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[69,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[69,6] <- 1
distance[69,12] <- length(index$Long)
distance[69,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[69,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[69,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[69,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0164 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[70,1] <- "Érablières & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[70,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[70,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[70,7] <- 1
distance[70,12] <- length(index$Long)
distance[70,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[70,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[70,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[70,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0164 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[71,1] <- "Érablières & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[71,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[71,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[71,8] <- 1
distance[71,12] <- length(index$Long)
distance[71,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[71,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[71,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[71,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0164 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[72,1] <- "Érablières & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[72,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[72,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[72,9] <- 1
distance[72,12] <- length(index$Long)
distance[72,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[72,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[72,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[72,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0164 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[73,1] <- "Érablières & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[73,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[73,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[73,10] <- 1
distance[73,12] <- length(index$Long)
distance[73,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[73,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[73,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[73,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(68:73),4] <- "Érablières"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Autres spécialités horticoles (0169) , echo=FALSE, message=FALSE,warning=FALSE}
df_0169 <- Req::Req_data(industry = 0169, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API

```

```{r Autres spécialités horticoles (0169) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[74,1] <- "Autres cultures horticoles"
distance[74,2] <- round(mean(df_0169$Long,na.rm = TRUE),5)
distance[74,3] <- round(mean(df_0169$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[74,5] <- 1
distance[74,12] <- length(df_0169$Long)
distance[74,13] <- round(mean(sqrt((df_0169$Lat - mean(df_0169$Lat,na.rm = TRUE))^2 + (df_0169$Long - mean(df_0169$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[74,14] <- round(sd(sqrt((df_0169$Lat - mean(df_0169$Lat,na.rm = TRUE))^2 + (df_0169$Long - mean(df_0169$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[74,15] <- round(sd(sqrt((df_0169$Lat - ecosysteme$Latitude[1])^2 + (df_0169$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[74,16] <- round(mean(sqrt((df_0169$Long - ecosysteme$Longitude)^2 + (df_0169$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0169 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[75,1] <- "Autres cultures horticoles & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[75,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[75,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[75,6] <- 1
distance[75,12] <- length(index$Long)
distance[75,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[75,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[75,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[75,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0169 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[76,1] <- "Autres cultures horticoles & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[76,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[76,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[76,7] <- 1
distance[76,12] <- length(index$Long)
distance[76,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[76,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[76,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[76,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0169 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[77,1] <- "Autres cultures horticoles & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[77,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[77,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[77,8] <- 1
distance[77,12] <- length(index$Long)
distance[77,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[77,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[77,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[77,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0169 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[78,1] <- "Autres cultures horticoles & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[78,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[78,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[78,9] <- 1
distance[78,12] <- length(index$Long)
distance[78,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[78,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[78,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[78,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0169 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[79,1] <- "Autres cultures horticoles & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[79,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[79,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[79,10] <- 1
distance[79,12] <- length(index$Long)
distance[79,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[79,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[79,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[79,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(74:79),4] <- "Autres cultures horticoles"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Élevages grandes cultures et productions horticoles (0171) , echo=FALSE, message=FALSE,warning=FALSE}
df_0171 <- Req::Req_data(industry = 0171, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Élevages grandes cultures et productions horticoles (0171) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[80,1] <- "Élevages, grandes cultures et productions horticoles"
distance[80,2] <- round(mean(df_0171$Long,na.rm = TRUE),5)
distance[80,3] <- round(mean(df_0171$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[80,5] <- 1
distance[80,12] <- length(df_0171$Long)
distance[80,13] <- round(mean(sqrt((df_0171$Lat - mean(df_0171$Lat,na.rm = TRUE))^2 + (df_0171$Long - mean(df_0171$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[80,14] <- round(sd(sqrt((df_0171$Lat - mean(df_0171$Lat,na.rm = TRUE))^2 + (df_0171$Long - mean(df_0171$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[80,15] <- round(sd(sqrt((df_0171$Lat - ecosysteme$Latitude[1])^2 + (df_0171$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[80,16] <- round(mean(sqrt((df_0171$Long - ecosysteme$Longitude)^2 + (df_0171$Lat - ecosysteme$Latitude)^2)),4)


index <- df_0171 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[81,1] <- "Élevages, grandes cultures et productions horticoles & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[81,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[81,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[81,6] <- 1
distance[81,12] <- length(index$Long)
distance[81,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[81,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[81,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[81,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0171 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[82,1] <- "Élevages, grandes cultures et productions horticoles & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[82,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[82,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[82,7] <- 1
distance[82,12] <- length(index$Long)
distance[82,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[82,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[82,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[82,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0171 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[83,1] <- "Élevages, grandes cultures et productions horticoles & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[83,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[83,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[83,8] <- 1
distance[83,12] <- length(index$Long)
distance[83,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[83,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[83,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[83,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0171 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[84,1] <- "Élevages, grandes cultures et productions horticoles & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[84,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[84,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[84,9] <- 1
distance[84,12] <- length(index$Long)
distance[84,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[84,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[84,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[84,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_0171 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[85,1] <- "Élevages, grandes cultures et productions horticoles & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[85,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[85,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[85,10] <- 1
distance[85,12] <- length(index$Long)
distance[85,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[85,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[85,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[85,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(80:85),4] <- "Élev., grandes cult. et prod. hort."
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r echo=FALSE, message = FALSE, warning = FALSE}
#.sidebar
# selectInput("sect_act", label = "Secteur d'activité",
#            choices = distance$Secteur, selected = distance$Secteur[1])

```

```{r echo=FALSE, message = FALSE, warning = FALSE}

##dist_user <- reactive({distance %>%
  #filter(Secteur ==input$sect_act)
  #})

#renderLeaflet({
 # leaflet(data = distance[which(distance$Secteur == input$sect_act),]) %>%
  #addTiles() %>%
  #addAwesomeMarkers(popup = ~Categorie)
#})
```

```{r df exploitations agricoles cumulees, echo=FALSE, message = FALSE, warning = FALSE}

df_tot <- full_join(df_0131,df_0134)
df_tot <- full_join(df_tot,df_0135)
df_tot <- full_join(df_tot,df_0138)
df_tot <- full_join(df_tot,df_0139)
df_tot <- full_join(df_tot,df_0151)
df_tot <- full_join(df_tot,df_0152)
df_tot <- full_join(df_tot,df_0159)
df_tot <- full_join(df_tot,df_0161)
df_tot <- full_join(df_tot,df_0162)
df_tot <- full_join(df_tot,df_0163)
df_tot <- full_join(df_tot,df_0164)
df_tot <- full_join(df_tot,df_0169)
df_tot <- full_join(df_tot,df_0171)
```

```{r exploitations agricoles cumulees 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[86,1] <- "Total des exploitations agricoles"
distance[86,2] <- round(mean(df_tot$Long,na.rm = TRUE),5)
distance[86,3] <- round(mean(df_tot$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[86,5] <- 1
distance[86,12] <- length(df_tot$Long)
distance[86,13] <- round(mean(sqrt((df_tot$Lat - mean(df_tot$Lat,na.rm = TRUE))^2 + (df_tot$Long - mean(df_tot$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[86,14] <- round(sd(sqrt((df_tot$Lat - mean(df_tot$Lat,na.rm = TRUE))^2 + (df_tot$Long - mean(df_tot$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[86,15] <- round(sd(sqrt((df_tot$Lat - ecosysteme$Latitude[1])^2 + (df_tot$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[86,16] <- round(mean(sqrt((df_tot$Long - ecosysteme$Longitude)^2 + (df_tot$Lat - ecosysteme$Latitude)^2)),4)

index <- df_tot %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[87,1] <- "Total des exploitations agricoles & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[87,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[87,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[87,6] <- 1
distance[87,12] <- length(index$Long)
distance[87,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[87,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[87,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[87,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_tot %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[88,1] <- "Total des exploitations agricoles & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[88,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[88,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[88,7] <- 1
distance[88,12] <- length(index$Long)
distance[88,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[88,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[88,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[88,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_tot %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[89,1] <- "Total des exploitations agricoles & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[89,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[89,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[89,8] <- 1
distance[89,12] <- length(index$Long)
distance[89,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[89,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[89,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[89,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_tot %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[90,1] <- "Total des exploitations agricoles & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[90,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[90,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[90,9] <- 1
distance[90,12] <- length(index$Long)
distance[90,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[90,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[90,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[90,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

index <- df_tot %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[91,1] <- "Total des exploitations agricoles & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[91,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[91,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[91,10] <- 1
distance[91,12] <- length(index$Long)
distance[91,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[91,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[91,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
distance[91,16] <- round(mean(sqrt((index$Long - ecosysteme$Longitude)^2 + (index$Lat - ecosysteme$Latitude)^2)),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(86:91),4] <- "Total des exploitations agricoles"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```


# Cartographie des secteurs

## Column {data-width="570"}

### Cartographie du centre des secteurs agricoles et du signal de l'écosystème d'innovation

```{r, echo=FALSE, message = FALSE, warning = FALSE}

index <- distance[-1,] %>%
  filter(Complet == 1)

leaflet(data = index) %>%
  addTiles() %>% 
  addCircleMarkers(lng = ecosysteme$Longitude[1], lat = ecosysteme$Latitude[1], ## On ajoute le centroïde de l'écosystème
      radius = 15, stroke = FALSE, fillOpacity = 0.9, 
      color = "steelblue", popup = ecosysteme$Nom_ent[1]) %>%
  
    addCircleMarkers(data = index[!index$Secteur %in% "Total des exploitations agricoles",],
                   lng = ~Longitude, lat = ~Latitude, color ="#D7301F",stroke = FALSE, fillOpacity = 0.8,
                   popup = ~paste("Nom :", Categorie, "<br/>","Distance avec l'écosystème", Dist_centre)) %>%
  
  addCircleMarkers(data = index[index$Secteur %in% "Total des exploitations agricoles",],
                   lng = ~Longitude, lat = ~Latitude, color = "#FC8D59" , stroke = FALSE, fillOpacity = 0.9, radius = 12,
                   popup = ~paste("Nom :", Categorie, "<br/>","Distance avec l'écosystème", Dist_centre)) %>%
      addLegend(
    "bottomright",
    title = "Légende",
    colors = c("steelblue","#D7301F","#FC8D59"),
   labels = c("Signal de l'écosystème d'innovation", "Centre des secteurs agricoles","Centre du total des exploitations agricoles"), 
    opacity = 0.9)

```

## Column { data-width="430"}

### Distance du centre des secteurs avec le signal de l'écosystème et nombre d'exploitations associées

```{r echo=FALSE, message = FALSE, warning = FALSE}
datatable(index[,c(1,11,12)],
            rownames = FALSE, 
              options = list(pageLength = 5, scrollX = TRUE,dom = 'tip'), 
              class = 'white-space: nowrap' )
```

### Cartographie de la distance moyenne des entreprises des sous-secteurs à l'écosystème et écart-type associé

```{r echo=FALSE, message = FALSE, warning = FALSE}

nb.cols <- 16
mycolors <- colorRampPalette(brewer.pal(8, "OrRd"))(nb.cols)
mycolors2 <- colorRampPalette(brewer.pal(11, "Spectral"))(nb.cols)

distance <- distance %>%
  mutate(Secteur = case_when(Secteur == "Élevages, grandes cultures et productions horticoles"~"Élev., grandes cult. et prod. hort.", TRUE ~ Secteur))

P <- distance[] %>%
  filter(Complet ==1) %>%
    ggplot() +
    geom_point(aes(moyenne_dist,ecart_type_dist,color=Secteur)) +
    xlab("Distance moyenne au centre du secteur") +
    ylab("Ecart-type au centre du secteur") +
    theme_minimal() +
    scale_color_manual(values = mycolors2)
      
  ggplotly(P)%>% #Ploty du graph
  config(displayModeBar = FALSE)
  
  
```

# Impact du nombre d'employé {data-navmenu="Analyse des variables"}

## Column {data-width = "330"}

### Graphique des distances moyennes des entreprises des secteur à l'écosystème en fonction de l'écart-type

```{r echo=FALSE, message = FALSE, warning = FALSE}

index <- distance[] %>%
  filter(Complet==1) 

p <-  ggplot(data = index) +
    geom_point(aes(dist_moy_centre,ecart_type_eco,color=Secteur)) +
    theme_minimal() +
    ylab("Ecart-type") +
    xlab("Distance moyenne au centre") +
    labs(colour = "Secteur") +
    scale_color_manual(values=mycolors2) +
    theme(axis.text.x = element_text(angle = 90))
    
  ggplotly(p)%>% #Ploty du graph
  config(displayModeBar = FALSE)
```

## Column {.tabset data-width="660"}

### Graphique des distances moyennes en fonction du nombre d'employé

```{r echo=FALSE, message = FALSE, warning = FALSE}
p <-  ggplot() +
    geom_point(data = distance[distance$emp_sup_10==1,],
               aes(Secteur,dist_moy_centre,color="> 10 employés")) +
    geom_point(data = distance[distance$emp_inf_10==1,],
               aes(Secteur,dist_moy_centre,color="< 10 employés")) +
    labs(colour = "Distance moyenne") +
    scale_color_manual(values = c("darkgreen","orange"),
                       labels = c("< 10 employés", "> 10 employés" )) +
    theme_minimal() +
    ylab("Valeur") +
    theme(axis.text.x = element_text(angle = 90))
  ggplotly(p)%>% #Ploty du graph
  config(displayModeBar = FALSE)
```

### Graphique des ecart-types en fonction du nombre d'employé

```{r echo=FALSE, message = FALSE, warning = FALSE}
 p <- ggplot() +
    geom_point(data = distance[distance$emp_sup_10==1,],
               aes(Secteur,ecart_type_eco,color = "> 10 employé" )) +
    geom_point(data = distance[distance$emp_inf_10==1,],
               aes(Secteur,ecart_type_eco,color = "< 10 employés" )) +
    labs(colour = "Écart-type") +
    scale_color_manual(values = c("darkgreen","orange"),
                       labels = c("< 10 employés", "> 10 employés")) +
    theme_minimal() +
    ylab("Valeur") +
    theme(axis.text.x = element_text(angle = 90))
  ggplotly(p)%>% #Ploty du graph
  config(displayModeBar = FALSE)
```

# Impact de l'âge {data-navmenu="Analyse des variables"}

## Rows {data-width = 660}

### Graphique des distances moyennes en fonction de l'âge des exploitations

```{r echo=FALSE, message = FALSE, warning = FALSE}
p<-  ggplot() +
    geom_point(data = distance[distance$age_inf_5==1,],
               aes(Secteur,dist_moy_centre,color="âge > 15 ans")) +
    geom_point(data = distance[distance$age_inf_15_sup_5==1,],
               aes(Secteur,dist_moy_centre,color="5 ans < âge < 15 ans")) +
      geom_point(data = distance[distance$age_sup_15==1,],
               aes(Secteur,dist_moy_centre,color="âge < 5 ans")) +
    labs(colour = "Distance moyenne") +
    scale_color_manual(values = c("red","darkgreen","orange"),
                       labels = c("âge < 5 ans","5 ans < âge < 15 ans", "âge > 15 ans" )) +
    theme_minimal() +
    ylab("Valeur") +
    theme(axis.text.x = element_text(angle = 90))
  ggplotly(p)%>% #Ploty du graph
  config(displayModeBar = FALSE)
```

## Rows {data-width = 660}

### Graphique des écart-types en fonction de l'âge des exploitations

```{r echo=FALSE, message = FALSE, warning = FALSE}

p<-  ggplot() +
    geom_point(data = distance[distance$age_inf_5==1,],
               aes(Secteur,ecart_type_eco,color="âge > 15 ans")) +
    geom_point(data = distance[distance$age_inf_15_sup_5==1,],
               aes(Secteur,ecart_type_eco,color="5 ans < âge < 15 ans")) +
      geom_point(data = distance[distance$age_sup_15==1,],
               aes(Secteur,ecart_type_eco,color="âge < 5 ans")) +
    labs(colour = "Écart-type") +
    scale_color_manual(values = c("red","darkgreen","orange"),
                       labels = c("âge < 5 ans", "5 ans < âge < 15 ans", "âge > 15 ans")) +
    theme_minimal() +
    ylab("Valeur") +
    theme(axis.text.x = element_text(angle = 90))
  ggplotly(p)%>% #Ploty du graph
  config(displayModeBar = FALSE)
```




# Repenser l'agriculture : Clusters agricoles

## Column {data-width="350"}

### Nombre optimal de clusters WSS
```{r preparation cluster 1, echo=FALSE, message = FALSE, warning = FALSE}
df_cluster <- df_tot[,c("NOM_ASSUJ","COD_ACT_ECON_CAE","COD_ACT_ECON_CAE2","Long","Lat")]
df_cluster <- df_cluster[!duplicated(df_cluster$NOM_ASSUJ),]
rownames(df_cluster) <-df_cluster$NOM_ASSUJ
fviz_nbclust(df_cluster[,c(4,5)], kmeans,method ="wss")

```

```{r preparation cluster 2, echo=FALSE, message = FALSE, warning = FALSE,include=FALSE}
fviz_nbclust(df_cluster[,c(4,5)], kmeans,method ="silhouette")
```


### Visualisation de la composition des clusters
```{r preparation cluster 3, echo=FALSE, message = FALSE, warning = FALSE,include=FALSE}
set.seed(123)
cluster1 <- kmeans(df_cluster[,c(4,5)],4, iter.max = 10, nstart = 2)
df_cluster <- cbind(df_cluster, cluster1$cluster)
colnames(df_cluster)[6] <-"cluster"
```

```{r preparation cluster 4, echo=FALSE, message = FALSE, warning = FALSE,include=FALSE}
distance_cluster <- as.data.frame(cluster1$centers)
distance_cluster[3] <- cluster1$size
distance_cluster[4] <- round(cluster1$withinss,4)
distance_cluster$Var1 <- c(1:4)
distance_cluster$Var1 <- as.factor(distance_cluster$Var1)
colnames(distance_cluster)[3] <- "size"  
colnames(distance_cluster)[4] <- "withinss"  

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 131 | COD_ACT_ECON_CAE2 == 131) 
distance_cluster$v131 <- table(index$cluster)


index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 134 | COD_ACT_ECON_CAE2 == 134) 
distance_cluster <- left_join(distance_cluster,as.data.frame(table(index$cluster)),by="Var1")
colnames(distance_cluster)[7] <- "v134"
distance_cluster$v134[3:4] <-0

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 135 | COD_ACT_ECON_CAE2 == 135) 
distance_cluster$v135 <- 0
distance_cluster$v135 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 138 | COD_ACT_ECON_CAE2 == 138) 
distance_cluster$v138 <- 0
distance_cluster$v138 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 139 | COD_ACT_ECON_CAE2 == 139) 
distance_cluster$v139 <- 0
distance_cluster$v139 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 151 | COD_ACT_ECON_CAE2 == 151) 
distance_cluster$v151 <- 0
distance_cluster$v151 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 152 | COD_ACT_ECON_CAE2 == 152)
distance_cluster$v152 <- 0
distance_cluster$v152 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 159 | COD_ACT_ECON_CAE2 == 159) 
distance_cluster$v159 <- 0
distance_cluster$v159 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 161 | COD_ACT_ECON_CAE2 == 161) 
distance_cluster$v161 <- 0
distance_cluster$v161 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 162 | COD_ACT_ECON_CAE2 == 162)
distance_cluster$v162 <- 0
distance_cluster$v162 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 163 | COD_ACT_ECON_CAE2 == 163) 
distance_cluster$v163 <- 0
distance_cluster$v163 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 164 | COD_ACT_ECON_CAE2 == 164) 
distance_cluster$v164 <- 0
distance_cluster$v164 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 169 | COD_ACT_ECON_CAE2 == 169) 
distance_cluster$v169 <- 0
distance_cluster$v169 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 171 | COD_ACT_ECON_CAE2 == 171) 
distance_cluster$v171 <- 0
distance_cluster$v171 <- table(index$cluster)

index <- as.data.frame(t(distance_cluster)) 
index <- index[-c(1:5),]
index$V1 <- as.numeric(index$V1)
index$V2 <- as.numeric(index$V2)
index$V3 <- as.numeric(index$V3)
index$V4 <- as.numeric(index$V4)
distance_cluster$sum <- colSums(index)
distance_cluster <- mutate(distance_cluster, withinss = round(withinss/size,4))


for (i in 6:19) {
  distance_cluster[,i] <- round(distance_cluster[,i] / distance_cluster$sum,4)
}

long_cluster <- gather(distance_cluster, key="Secteur", value = "pourcentage", -Long,-Lat,-size,-withinss, -Var1,-sum)

long_cluster <- long_cluster %>%
  mutate(Secteur = case_when(Secteur == "v131" ~ "Céréales",Secteur == "v134" ~ "Maïs", Secteur == "v135" ~ "Plantes fourragères",
                             Secteur == "v138" ~ "Pommes de terre", Secteur == "v139" ~ "Autres grandes cultures", Secteur == "v151" ~ "Fruits",
                             Secteur == "v152" ~ "Légumes", Secteur == "v159" ~ "Mixte fruits et légumes", Secteur == "v161" ~ "Champignons",
                             Secteur == "v162" ~ "Serres", Secteur == "v163" ~ "Pépinières", Secteur == "v164" ~ "Érablières",
                             Secteur == "v169" ~ "Autres cultures horticoles", Secteur == "v171" ~ "Élev., grandes cult. et prod. hort.",
                             TRUE ~Secteur))

```


```{r, echo=FALSE, message = FALSE, warning = FALSE}
##
p <- ggplot(long_cluster) +
  geom_bar(aes(x=Var1,y=pourcentage,fill =Secteur),stat="identity") +
  coord_flip() +
  xlab("Numero de cluster") +
  theme_minimal() +
  scale_fill_manual(values = mycolors2)

ggplotly(p)%>% #Ploty du graph
  config(displayModeBar = FALSE)
```

## Column {data-width="650"}

```{r, echo=FALSE, message = FALSE, warning = FALSE, include = FALSE}
getColor <- function(df_cluster) {
  sapply(df_cluster$cluster, function(cluster) {
  if(cluster == 1) {
'#d7191c'
    } else if(cluster == 2) {
'#fdae61'
  } else if(cluster == 3) {
"#1b7837"
  } else if(cluster == 4) { 
'#2b83ba'
  } })
}

df_cluster <- arrange(df_cluster,cluster)
df_cluster[7] <- getColor(df_cluster)
colnames(df_cluster)[7] <- "couleur"

index <- df_cluster %>%
  filter(cluster == 1)
distance_cluster$withinss[1] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance_cluster$ecart_type[1] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)

index <- df_cluster %>%
  filter(cluster == 2)
distance_cluster$withinss[2] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance_cluster$ecart_type[2] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)

index <- df_cluster %>%
  filter(cluster == 3)
distance_cluster$withinss[3] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance_cluster$ecart_type[3] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)

index <- df_cluster %>%
  filter(cluster == 4)
distance_cluster$withinss[4] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance_cluster$ecart_type[4] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)

df_cluster <- df_cluster %>%
  mutate(cluster = case_when(cluster == 1 ~ "Cluster 1",cluster == 2 ~ "Cluster 2",
                              cluster == 3 ~ "Cluster 3",cluster == 4 ~ "Cluster 4"))
```

### Visualisation des entreprises
```{r visualisation cluster 1, echo=FALSE, message = FALSE, warning = FALSE}

leaflet(data = df_cluster) %>% 
  addTiles() %>% 
  addCircleMarkers(lng = ~Long, lat = ~Lat, color = df_cluster$couleur, popup = ~rownames(df_cluster)) %>%
  addLegend(
    "bottomright",
    title = "Appartenance des entreprises",
    colors = unique(df_cluster$couleur),
    labels = unique(df_cluster$cluster),
    opacity = 0.8) 

```



### Visualisation des clusters
```{r, echo=FALSE, message = FALSE, warning = FALSE}

#table(df_cluster$COD_ACT_ECON_CAE2,df_cluster$cluster)
col_cluster <-c('#fcbba1','#fc9272','#fb6a4a','#de2d26','#a50f15')


leaflet(data=distance_cluster) %>%
  addTiles() %>%
  addCircleMarkers(lng = ~Long, lat = ~Lat,radius = ~15*withinss,
                   color =~colorBin(col_cluster,size)(size), stroke = TRUE, fillOpacity = 0.7,
                   popup = ~paste("Distance moyenne:",withinss, "<br/>","taille du cluster:", size)) %>%
  addCircleMarkers(lng = ecosysteme$Longitude[1], lat = ecosysteme$Latitude[1], ## On ajoute le centroïde de l'écosystème
      radius = 15, stroke = FALSE, fillOpacity = 0.9, 
      color = "steelblue", popup = ecosysteme$Nom_ent[1]) %>%
  addLegend(
    "bottomleft",
    title = "Taille du cluster",
    pal = colorBin(col_cluster, distance_cluster$size,5),
    values = distance_cluster$size, 
    opacity = 0.9)

```